Overview

Dataset statistics

Number of variables12
Number of observations7111
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory666.8 KiB
Average record size in memory96.0 B

Variable types

DateTime1
Numeric11

Alerts

deg_C is highly correlated with relative_humidityHigh correlation
relative_humidity is highly correlated with deg_CHigh correlation
absolute_humidity is highly correlated with sensor_4High correlation
sensor_1 is highly correlated with sensor_2 and 6 other fieldsHigh correlation
sensor_2 is highly correlated with sensor_1 and 6 other fieldsHigh correlation
sensor_3 is highly correlated with sensor_1 and 6 other fieldsHigh correlation
sensor_4 is highly correlated with absolute_humidity and 6 other fieldsHigh correlation
sensor_5 is highly correlated with sensor_1 and 6 other fieldsHigh correlation
target_carbon_monoxide is highly correlated with sensor_1 and 6 other fieldsHigh correlation
target_benzene is highly correlated with sensor_1 and 6 other fieldsHigh correlation
target_nitrogen_oxides is highly correlated with sensor_1 and 5 other fieldsHigh correlation
deg_C is highly correlated with relative_humidityHigh correlation
relative_humidity is highly correlated with deg_CHigh correlation
absolute_humidity is highly correlated with sensor_4High correlation
sensor_1 is highly correlated with sensor_2 and 6 other fieldsHigh correlation
sensor_2 is highly correlated with sensor_1 and 6 other fieldsHigh correlation
sensor_3 is highly correlated with sensor_1 and 5 other fieldsHigh correlation
sensor_4 is highly correlated with absolute_humidity and 6 other fieldsHigh correlation
sensor_5 is highly correlated with sensor_1 and 6 other fieldsHigh correlation
target_carbon_monoxide is highly correlated with sensor_1 and 6 other fieldsHigh correlation
target_benzene is highly correlated with sensor_1 and 6 other fieldsHigh correlation
target_nitrogen_oxides is highly correlated with sensor_1 and 4 other fieldsHigh correlation
sensor_1 is highly correlated with sensor_2 and 5 other fieldsHigh correlation
sensor_2 is highly correlated with sensor_1 and 6 other fieldsHigh correlation
sensor_3 is highly correlated with sensor_1 and 6 other fieldsHigh correlation
sensor_4 is highly correlated with sensor_2 and 2 other fieldsHigh correlation
sensor_5 is highly correlated with sensor_1 and 5 other fieldsHigh correlation
target_carbon_monoxide is highly correlated with sensor_1 and 5 other fieldsHigh correlation
target_benzene is highly correlated with sensor_1 and 6 other fieldsHigh correlation
target_nitrogen_oxides is highly correlated with sensor_1 and 5 other fieldsHigh correlation
deg_C is highly correlated with relative_humidity and 2 other fieldsHigh correlation
relative_humidity is highly correlated with deg_CHigh correlation
absolute_humidity is highly correlated with deg_C and 3 other fieldsHigh correlation
sensor_1 is highly correlated with sensor_2 and 6 other fieldsHigh correlation
sensor_2 is highly correlated with absolute_humidity and 7 other fieldsHigh correlation
sensor_3 is highly correlated with absolute_humidity and 7 other fieldsHigh correlation
sensor_4 is highly correlated with deg_C and 8 other fieldsHigh correlation
sensor_5 is highly correlated with sensor_1 and 6 other fieldsHigh correlation
target_carbon_monoxide is highly correlated with sensor_1 and 6 other fieldsHigh correlation
target_benzene is highly correlated with sensor_1 and 6 other fieldsHigh correlation
target_nitrogen_oxides is highly correlated with sensor_1 and 6 other fieldsHigh correlation
date_time has unique values Unique

Reproduction

Analysis started2021-10-05 10:15:37.329409
Analysis finished2021-10-05 10:15:53.865165
Duration16.54 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

date_time
Date

UNIQUE

Distinct7111
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size55.7 KiB
Minimum2010-03-10 18:00:00
Maximum2011-01-01 00:00:00
2021-10-05T12:15:53.938167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:54.070166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

deg_C
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct408
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.87803403
Minimum1.3
Maximum46.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2021-10-05T12:15:54.211779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile8.8
Q114.9
median20.7
Q325.8
95-th percentile35.6
Maximum46.1
Range44.8
Interquartile range (IQR)10.9

Descriptive statistics

Standard deviation7.937916707
Coefficient of variation (CV)0.380204223
Kurtosis-0.3130863355
Mean20.87803403
Median Absolute Deviation (MAD)5.5
Skewness0.2903180452
Sum148463.7
Variance63.01052165
MonotonicityNot monotonic
2021-10-05T12:15:54.336820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2156
 
0.8%
20.450
 
0.7%
25.450
 
0.7%
22.549
 
0.7%
2548
 
0.7%
21.347
 
0.7%
24.546
 
0.6%
24.345
 
0.6%
22.842
 
0.6%
23.842
 
0.6%
Other values (398)6636
93.3%
ValueCountFrequency (%)
1.32
< 0.1%
1.41
< 0.1%
1.51
< 0.1%
1.71
< 0.1%
2.21
< 0.1%
2.32
< 0.1%
2.52
< 0.1%
2.61
< 0.1%
2.91
< 0.1%
31
< 0.1%
ValueCountFrequency (%)
46.11
 
< 0.1%
45.31
 
< 0.1%
45.21
 
< 0.1%
44.11
 
< 0.1%
43.21
 
< 0.1%
43.11
 
< 0.1%
433
< 0.1%
42.91
 
< 0.1%
42.81
 
< 0.1%
42.71
 
< 0.1%

relative_humidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct762
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.56100408
Minimum8.9
Maximum90.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2021-10-05T12:15:54.462817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8.9
5-th percentile19.8
Q133.7
median47.3
Q360.8
95-th percentile77.05
Maximum90.8
Range81.9
Interquartile range (IQR)27.1

Descriptive statistics

Standard deviation17.39873072
Coefficient of variation (CV)0.3658192475
Kurtosis-0.8188864778
Mean47.56100408
Median Absolute Deviation (MAD)13.6
Skewness0.07862123326
Sum338206.3
Variance302.7158307
MonotonicityNot monotonic
2021-10-05T12:15:54.569817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.525
 
0.4%
50.624
 
0.3%
3923
 
0.3%
34.623
 
0.3%
47.323
 
0.3%
43.623
 
0.3%
46.921
 
0.3%
51.320
 
0.3%
46.420
 
0.3%
31.120
 
0.3%
Other values (752)6889
96.9%
ValueCountFrequency (%)
8.91
< 0.1%
91
< 0.1%
9.21
< 0.1%
9.31
< 0.1%
9.51
< 0.1%
9.81
< 0.1%
10.31
< 0.1%
10.42
< 0.1%
10.51
< 0.1%
11.11
< 0.1%
ValueCountFrequency (%)
90.82
< 0.1%
89.61
< 0.1%
89.12
< 0.1%
88.71
< 0.1%
88.61
< 0.1%
882
< 0.1%
87.81
< 0.1%
87.52
< 0.1%
87.32
< 0.1%
86.91
< 0.1%

absolute_humidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5451
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.110308761
Minimum0.1988
Maximum2.231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2021-10-05T12:15:54.690820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1988
5-th percentile0.42105
Q10.8559
median1.0835
Q31.40415
95-th percentile1.75615
Maximum2.231
Range2.0322
Interquartile range (IQR)0.54825

Descriptive statistics

Standard deviation0.3989500846
Coefficient of variation (CV)0.3593145426
Kurtosis-0.3195498508
Mean1.110308761
Median Absolute Deviation (MAD)0.267
Skewness-0.03589043194
Sum7895.4056
Variance0.15916117
MonotonicityNot monotonic
2021-10-05T12:15:54.815850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.22589
 
0.1%
0.22579
 
0.1%
0.22569
 
0.1%
0.22636
 
0.1%
1.11996
 
0.1%
0.22626
 
0.1%
0.2285
 
0.1%
0.22615
 
0.1%
0.22595
 
0.1%
0.22545
 
0.1%
Other values (5441)7046
99.1%
ValueCountFrequency (%)
0.19881
< 0.1%
0.20291
< 0.1%
0.21361
< 0.1%
0.21461
< 0.1%
0.21481
< 0.1%
0.21631
< 0.1%
0.21671
< 0.1%
0.21691
< 0.1%
0.2171
< 0.1%
0.2181
< 0.1%
ValueCountFrequency (%)
2.2311
< 0.1%
2.18061
< 0.1%
2.17661
< 0.1%
2.17191
< 0.1%
2.13951
< 0.1%
2.13621
< 0.1%
2.12471
< 0.1%
2.11951
< 0.1%
2.1171
< 0.1%
2.11641
< 0.1%

sensor_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3882
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1091.5721
Minimum620.3
Maximum2088.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2021-10-05T12:15:54.954816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum620.3
5-th percentile796.85
Q1930.25
median1060.5
Q31215.8
95-th percentile1502.55
Maximum2088.3
Range1468
Interquartile range (IQR)285.55

Descriptive statistics

Standard deviation218.5375542
Coefficient of variation (CV)0.2002044155
Kurtosis0.6151993578
Mean1091.5721
Median Absolute Deviation (MAD)137.8
Skewness0.7959241429
Sum7762169.2
Variance47758.66258
MonotonicityNot monotonic
2021-10-05T12:15:55.070363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100712
 
0.2%
1054.710
 
0.1%
93010
 
0.1%
982.69
 
0.1%
10509
 
0.1%
951.48
 
0.1%
980.78
 
0.1%
927.28
 
0.1%
9708
 
0.1%
1150.48
 
0.1%
Other values (3872)7021
98.7%
ValueCountFrequency (%)
620.31
< 0.1%
633.31
< 0.1%
634.11
< 0.1%
635.21
< 0.1%
635.41
< 0.1%
640.31
< 0.1%
655.61
< 0.1%
660.31
< 0.1%
664.32
< 0.1%
665.51
< 0.1%
ValueCountFrequency (%)
2088.31
< 0.1%
2022.51
< 0.1%
2021.61
< 0.1%
1999.21
< 0.1%
1963.11
< 0.1%
1957.31
< 0.1%
19561
< 0.1%
1953.61
< 0.1%
1953.31
< 0.1%
19501
< 0.1%

sensor_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4254
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean938.0649698
Minimum364
Maximum2302.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2021-10-05T12:15:55.197396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum364
5-th percentile512.6
Q1734.9
median914.2
Q31124.1
95-th percentile1434.5
Maximum2302.6
Range1938.6
Interquartile range (IQR)389.2

Descriptive statistics

Standard deviation281.9789878
Coefficient of variation (CV)0.3005964373
Kurtosis0.1166562533
Mean938.0649698
Median Absolute Deviation (MAD)192.5
Skewness0.4178397604
Sum6670580
Variance79512.14959
MonotonicityNot monotonic
2021-10-05T12:15:55.312397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
373.712
 
0.2%
401.311
 
0.2%
365.910
 
0.1%
377.79
 
0.1%
4179
 
0.1%
393.48
 
0.1%
946.68
 
0.1%
1081.67
 
0.1%
1074.27
 
0.1%
413.17
 
0.1%
Other values (4244)7023
98.8%
ValueCountFrequency (%)
3641
 
< 0.1%
364.82
 
< 0.1%
364.91
 
< 0.1%
365.11
 
< 0.1%
365.31
 
< 0.1%
365.83
 
< 0.1%
365.910
0.1%
366.11
 
< 0.1%
368.81
 
< 0.1%
3691
 
< 0.1%
ValueCountFrequency (%)
2302.61
< 0.1%
20791
< 0.1%
20571
< 0.1%
1966.91
< 0.1%
1941.41
< 0.1%
1938.71
< 0.1%
1923.51
< 0.1%
1913.31
< 0.1%
1907.91
< 0.1%
1903.31
< 0.1%

sensor_3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4251
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean883.9033047
Minimum310.6
Maximum2567.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2021-10-05T12:15:55.439396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum310.6
5-th percentile496.75
Q1681.05
median827.8
Q31008.85
95-th percentile1537.1
Maximum2567.4
Range2256.8
Interquartile range (IQR)327.8

Descriptive statistics

Standard deviation310.4563551
Coefficient of variation (CV)0.3512333911
Kurtosis2.61958281
Mean883.9033047
Median Absolute Deviation (MAD)159.9
Skewness1.407229292
Sum6285436.4
Variance96383.14843
MonotonicityNot monotonic
2021-10-05T12:15:55.574096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
832.39
 
0.1%
776.29
 
0.1%
8318
 
0.1%
7408
 
0.1%
8908
 
0.1%
8147
 
0.1%
8167
 
0.1%
6517
 
0.1%
1045.47
 
0.1%
765.47
 
0.1%
Other values (4241)7034
98.9%
ValueCountFrequency (%)
310.61
< 0.1%
317.11
< 0.1%
323.41
< 0.1%
327.11
< 0.1%
328.21
< 0.1%
328.41
< 0.1%
331.31
< 0.1%
331.51
< 0.1%
3351
< 0.1%
3401
< 0.1%
ValueCountFrequency (%)
2567.41
< 0.1%
2548.81
< 0.1%
2533.41
< 0.1%
2466.61
< 0.1%
2400.91
< 0.1%
2225.31
< 0.1%
2225.21
< 0.1%
2169.81
< 0.1%
2164.21
< 0.1%
21211
< 0.1%

sensor_4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4655
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1513.238349
Minimum552.9
Maximum2913.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2021-10-05T12:15:55.709702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum552.9
5-th percentile907.35
Q11320.35
median1513.1
Q31720.4
95-th percentile2083.15
Maximum2913.8
Range2360.9
Interquartile range (IQR)400.05

Descriptive statistics

Standard deviation350.18031
Coefficient of variation (CV)0.2314112051
Kurtosis0.8087355384
Mean1513.238349
Median Absolute Deviation (MAD)199.6
Skewness-0.1266305599
Sum10760637.9
Variance122626.2495
MonotonicityNot monotonic
2021-10-05T12:15:56.001740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1458.29
 
0.1%
14889
 
0.1%
1455.48
 
0.1%
16167
 
0.1%
1612.17
 
0.1%
15117
 
0.1%
1426.97
 
0.1%
16816
 
0.1%
1277.86
 
0.1%
1364.26
 
0.1%
Other values (4645)7039
99.0%
ValueCountFrequency (%)
552.91
< 0.1%
553.11
< 0.1%
554.21
< 0.1%
554.31
< 0.1%
5571
< 0.1%
559.91
< 0.1%
5611
< 0.1%
561.21
< 0.1%
562.11
< 0.1%
562.61
< 0.1%
ValueCountFrequency (%)
2913.81
< 0.1%
2779.31
< 0.1%
2773.51
< 0.1%
2746.61
< 0.1%
2722.31
< 0.1%
2712.51
< 0.1%
2690.11
< 0.1%
2688.21
< 0.1%
26881
< 0.1%
2664.11
< 0.1%

sensor_5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4839
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean998.3355646
Minimum242.7
Maximum2594.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2021-10-05T12:15:56.123706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum242.7
5-th percentile476.8
Q1722.85
median928.7
Q31224.7
95-th percentile1715.5
Maximum2594.6
Range2351.9
Interquartile range (IQR)501.85

Descriptive statistics

Standard deviation381.5376954
Coefficient of variation (CV)0.382173799
Kurtosis0.4004843033
Mean998.3355646
Median Absolute Deviation (MAD)241.1
Skewness0.7682900442
Sum7099164.2
Variance145571.013
MonotonicityNot monotonic
2021-10-05T12:15:56.255714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9117
 
0.1%
8947
 
0.1%
658.46
 
0.1%
8846
 
0.1%
8396
 
0.1%
7996
 
0.1%
944.56
 
0.1%
724.26
 
0.1%
812.26
 
0.1%
930.26
 
0.1%
Other values (4829)7049
99.1%
ValueCountFrequency (%)
242.71
< 0.1%
257.71
< 0.1%
265.31
< 0.1%
268.51
< 0.1%
271.31
< 0.1%
283.91
< 0.1%
285.51
< 0.1%
293.91
< 0.1%
294.61
< 0.1%
294.91
< 0.1%
ValueCountFrequency (%)
2594.61
< 0.1%
25231
< 0.1%
2514.31
< 0.1%
25071
< 0.1%
2496.81
< 0.1%
24651
< 0.1%
2450.21
< 0.1%
2421.31
< 0.1%
2405.61
< 0.1%
2370.21
< 0.1%

target_carbon_monoxide
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct95
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.086218535
Minimum0.1
Maximum12.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2021-10-05T12:15:56.392706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11
median1.7
Q32.8
95-th percentile4.9
Maximum12.5
Range12.4
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.44710922
Coefficient of variation (CV)0.6936517894
Kurtosis3.096547119
Mean2.086218535
Median Absolute Deviation (MAD)0.8
Skewness1.469212986
Sum14835.1
Variance2.094125094
MonotonicityNot monotonic
2021-10-05T12:15:56.534708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1286
 
4.0%
0.7280
 
3.9%
1.6276
 
3.9%
0.8261
 
3.7%
0.6260
 
3.7%
1.2256
 
3.6%
1.5254
 
3.6%
1.4243
 
3.4%
1.8243
 
3.4%
0.5237
 
3.3%
Other values (85)4515
63.5%
ValueCountFrequency (%)
0.117
 
0.2%
0.228
 
0.4%
0.393
 
1.3%
0.4189
2.7%
0.5237
3.3%
0.6260
3.7%
0.7280
3.9%
0.8261
3.7%
0.9232
3.3%
1286
4.0%
ValueCountFrequency (%)
12.51
< 0.1%
121
< 0.1%
101
< 0.1%
9.92
< 0.1%
9.81
< 0.1%
9.61
< 0.1%
9.51
< 0.1%
9.11
< 0.1%
8.92
< 0.1%
8.82
< 0.1%

target_benzene
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct405
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.23708339
Minimum0.1
Maximum63.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2021-10-05T12:15:56.671735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.2
Q14.5
median8.5
Q314.2
95-th percentile25.05
Maximum63.7
Range63.6
Interquartile range (IQR)9.7

Descriptive statistics

Standard deviation7.694425724
Coefficient of variation (CV)0.7516228431
Kurtosis2.428752107
Mean10.23708339
Median Absolute Deviation (MAD)4.6
Skewness1.324939317
Sum72795.9
Variance59.20418722
MonotonicityNot monotonic
2021-10-05T12:15:56.785708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1228
 
3.2%
5.668
 
1.0%
2.864
 
0.9%
3.862
 
0.9%
6.758
 
0.8%
5.256
 
0.8%
3.155
 
0.8%
3.955
 
0.8%
753
 
0.7%
8.253
 
0.7%
Other values (395)6359
89.4%
ValueCountFrequency (%)
0.1228
3.2%
0.22
 
< 0.1%
0.32
 
< 0.1%
0.48
 
0.1%
0.511
 
0.2%
0.610
 
0.1%
0.723
 
0.3%
0.816
 
0.2%
0.913
 
0.2%
116
 
0.2%
ValueCountFrequency (%)
63.71
< 0.1%
50.52
< 0.1%
49.92
< 0.1%
48.71
< 0.1%
48.21
< 0.1%
48.11
< 0.1%
47.81
< 0.1%
47.71
< 0.1%
47.51
< 0.1%
45.41
< 0.1%

target_nitrogen_oxides
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3268
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean204.0667839
Minimum1.9
Maximum1472.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2021-10-05T12:15:56.911742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile31.5
Q176.45
median141
Q3260
95-th percentile605.95
Maximum1472.3
Range1470.4
Interquartile range (IQR)183.55

Descriptive statistics

Standard deviation193.9277234
Coefficient of variation (CV)0.9503149886
Kurtosis6.005939643
Mean204.0667839
Median Absolute Deviation (MAD)78.1
Skewness2.177252062
Sum1451118.9
Variance37607.9619
MonotonicityNot monotonic
2021-10-05T12:15:57.023953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9714
 
0.2%
109.214
 
0.2%
44.614
 
0.2%
10013
 
0.2%
92.112
 
0.2%
106.112
 
0.2%
12611
 
0.2%
54.111
 
0.2%
59.211
 
0.2%
68.611
 
0.2%
Other values (3258)6988
98.3%
ValueCountFrequency (%)
1.91
< 0.1%
3.91
< 0.1%
6.21
< 0.1%
6.71
< 0.1%
8.31
< 0.1%
8.61
< 0.1%
91
< 0.1%
9.61
< 0.1%
101
< 0.1%
10.31
< 0.1%
ValueCountFrequency (%)
1472.32
< 0.1%
14051
< 0.1%
13581
< 0.1%
1354.51
< 0.1%
1341.61
< 0.1%
1336.21
< 0.1%
13271
< 0.1%
1304.61
< 0.1%
1296.91
< 0.1%
12611
< 0.1%

Interactions

2021-10-05T12:15:52.041436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:38.052498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:39.478778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:40.692779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:42.142060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:43.714683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:45.166148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:46.498644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:47.895860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:49.212085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:50.563731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:52.162435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:38.182498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:39.588778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:40.812781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:42.257089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:43.848710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:45.278984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:46.607671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:48.003893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:49.324084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:50.679730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:52.275438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:38.293164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:39.684811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:40.931869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:42.456069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:43.965682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:45.395218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:46.806644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:48.107897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:49.441083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:50.788770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:52.417470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:38.428166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:39.807779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:41.083086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:42.605177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:44.110682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:45.527099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:46.941644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:48.250903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:49.584731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:50.939733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:52.547466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:38.557144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:39.924779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:41.225057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:42.750792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:44.252279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:45.648101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:47.067890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:48.393285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:49.714758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:51.070437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:52.679437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:38.695719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:40.043779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:41.363085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:42.895792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:44.398626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:45.769128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:47.193899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:48.520287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:49.842733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:51.202439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:52.796435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:38.808813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:40.146779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:41.486057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:43.020986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:44.521632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:45.879108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:47.299890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:48.635262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:49.954763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:51.322436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:52.920435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:38.924891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:40.252811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:41.617057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:43.152987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:44.646631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:45.996132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:47.414862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:48.754260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:50.069731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:51.442463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:53.039193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:39.105145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:40.354812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:41.738057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:43.276987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:44.770631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:46.106099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:47.523860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:48.862262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:50.183730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:51.555435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:53.167166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:39.233779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:40.463781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:41.870091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:43.410988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:44.904632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:46.251644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:47.642893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:48.976479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:50.310730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:51.680437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:53.297198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:39.353779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:40.574781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:42.001089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:43.548005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:45.029150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:46.376644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:47.761889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:49.092448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:50.432733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-05T12:15:51.914470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-10-05T12:15:57.137955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-05T12:15:57.336986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-05T12:15:57.540990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-05T12:15:57.742981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-05T12:15:53.511164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-05T12:15:53.764198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

date_timedeg_Crelative_humidityabsolute_humiditysensor_1sensor_2sensor_3sensor_4sensor_5target_carbon_monoxidetarget_benzenetarget_nitrogen_oxides
02010-03-10 18:00:0013.146.00.75781387.21087.81056.01742.81293.42.512.0167.7
12010-03-10 19:00:0013.245.30.72551279.1888.21197.51449.91010.92.19.998.9
22010-03-10 20:00:0012.656.20.75021331.9929.61060.21586.11117.02.29.2127.1
32010-03-10 21:00:0011.062.40.78671321.0929.01102.91536.51263.22.29.7177.2
42010-03-10 22:00:0011.959.00.78881272.0852.71180.91415.51132.21.56.4121.8
52010-03-10 23:00:0011.256.80.78481220.9697.51417.21462.6949.01.24.488.1
62010-03-11 00:00:0010.755.70.76031244.2669.31491.21413.0769.61.23.759.5
72010-03-11 01:00:0010.357.00.77021181.4631.71511.11359.7715.41.03.463.9
82010-03-11 02:00:0010.162.70.76481159.6602.91610.61212.2657.20.92.246.4
92010-03-11 03:00:0010.559.60.75171030.2521.71790.21148.6491.00.61.643.0

Last rows

date_timedeg_Crelative_humidityabsolute_humiditysensor_1sensor_2sensor_3sensor_4sensor_5target_carbon_monoxidetarget_benzenetarget_nitrogen_oxides
71012010-12-31 15:00:0012.129.80.4160828.4760.0991.0882.0597.90.94.3165.9
71022010-12-31 16:00:0012.425.60.4185926.2746.4843.5974.4769.61.15.8199.4
71032010-12-31 17:00:0012.129.30.41481000.5883.0834.4926.3913.91.47.4228.5
71042010-12-31 18:00:0010.232.00.4112922.7800.7856.5876.1819.81.55.9206.1
71052010-12-31 19:00:009.134.30.3958957.9741.9970.3915.1866.01.24.9211.0
71062010-12-31 20:00:009.232.00.38711000.5811.2873.0909.0910.51.35.1191.1
71072010-12-31 21:00:009.133.20.37661022.7790.0951.6912.9903.41.45.8221.3
71082010-12-31 22:00:009.634.60.43101044.4767.3861.9889.21159.11.65.2227.4
71092010-12-31 23:00:008.040.70.4085952.8691.9908.5917.01206.31.54.6199.8
71102011-01-01 00:00:008.041.30.43751108.8745.7797.1880.01273.11.44.1186.5